Threat Classification Using Deep Learning

To classify cybersecurity threats, supervised learning models use labeled datasets, where attack types are predefined. The pipeline includes:

• Data Tokenization: Converting raw network logs into numerical sequences.
• Feature Extraction: Utilizing embeddings for malware detection or intrusion classification.
• Model Training: Applying Softmax, Sigmoid, or multi-label classification techniques.
• Evaluation Metrics: Measuring accuracy, precision, recall, and F1-score to optimize model performance.